Vision systems that perform measurement, inspection, alignment of objects and/or decoding of symbology (e.g. one-dimensional and two-dimensional bar codes—also termed “IDs”) are used in a wide range of applications and industries. These systems are based around the use of an image sensor (also termed an “imager”), which acquires images (typically grayscale or color, and in one, two or three dimensions) of the subject or object, and processes these acquired images using an on-board or interconnected vision system processor. The processor generally includes both processing hardware and non-transitory computer-readable program instructions that perform one or more vision system processes to generate a desired output based upon the image's processed information. This image information is typically provided within an array of image pixels each having various colors and/or intensities. In the example of an ID reader (also termed herein, a “camera”), the user or automated process acquires an image of an object that is believed to contain one or more barcodes, 2D codes or other symbol types. The image is processed to identify code features, which are then decoded by a decoding process and/or processor obtain the inherent alphanumeric data represented by the code.
A common use for ID readers is to track and sort objects moving along a line (e.g. a conveyor) in manufacturing and logistics operations. The ID reader can be positioned over the line at an appropriate viewing angle to acquire any expected IDs on respective objects as they each move through the field of view. The focal distance of the reader with respect to the object can vary, depending on the placement of the reader relative to the moving line and the size (i.e. height) of the object. That is, larger objects may cause IDs thereon to be located closer to the reader, while smaller/flatter objects may contain IDs that are further from the reader. In each case, the ID should appear with sufficient resolution to be properly imaged and decoded. Disadvantageously, most commercially available image sensors, upon which vision system cameras are based, define a pixel array that is nearly square in dimension (e.g. nearly a 1:1 aspect ratio of width-to-height, and more typically a ratio of 4:3, 5:4 or 16:9). This width/height ratio does not fit well with the requirements of a reading application in which objects on a wide conveyor line pass with respect to the camera's field of view (FOV). More generally, height of the FOV should be slightly larger than the ID (or other region of interest), while the width of the FOV should be approximately equal or slightly greater than to that of the conveyor line. In some instances, a line-scan camera can be employed to address object movement and a wide filed of view. However, such solutions are not applicable for certain object geometries and line arrangements. Likewise, line scan (i.e. one-dimensional) image sensors tend to be more costly than conventional rectangular format sensors.
Where an object and/or the line is relatively wide, the lens and imager of a single ID reader may not have sufficient field of view in the widthwise direction to cover the entire width of the line while maintaining needed resolution for accurate imaging and decoding of IDs. Failure to image the full width can cause the reader to miss reading IDs that are outside of the field of view, or that pass through the field too quickly. A costly approach to provide the needed width is to employ multiple cameras across the width of the line, typically networked together to share image data and processes. A wider aspect ratio for the FOV of one or more cameras can alternatively be achieved by optically expanding the native FOV of the sensor using a field of view expander that splits the field into a plurality of narrower strips that stretch across the width of the conveyor line. A challenge in providing such an arrangement is that a narrower field in the upstream-to-downstream direction of the moving line may require a higher frame rate to ensure an ID is properly captured before it passes out of the field. This can tax the processing speed of the system and current imager-based decoding systems that acquire over a wide area basically lack the frame rate needed for reliable decoding at high object-throughput speed.
A further challenge in operating a vision-system-based ID reader is that focus and illumination should be set to relatively optimal values in order to provide a readable image of IDs to the decoding application. This entails rapid analysis of the focal distance and the lighting conditions so that these parameters can be automatically accounted for and/or automatically adjusted. Where fields of view are wide and/or the throughput of objects relative to the imaged scene is high, the processing speed needed to perform such functions may be unavailable using conventional vision-system based readers.
In general, imagers/sensors can acquire images at a relatively high frame rate to accommodate such high-speed functions. It is generally desirable to provide image processing arrangements/procedures that can more-effectively employ image frames in a variety of manners that enhance the ability of the system to adjust parameters and read image data at a higher rate of speed.